Abstract
The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection platform that may be used for patient activity recognition and emergency treatment. Both visual data captured from the user's environment and motion data collected from the subject's body are utilized. Visual information is acquired using overhead cameras, while motion data is collected from on-body sensors. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects. Acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event. Support Vector Machines (SVM) classification methodology has been evaluated using the latter acceleration and visual trajectory data. The performance of the classifier has been assessed in terms of accuracy and efficiency and results are presented.
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Noury N., Herve T., Rialle V., Virone G., Mercier E., Morey G., Moro A., Porcheron T., “Monitoring behavior in home using a smart fall sensor and position sensors”, In Proc. 1st Annual International Conference on Microtechnologies in Medicine and Biology, pp. 607–610, Oct. 2000.
Noury N., “A smart sensor for the remote follow up of activity and fall detection of the elderly”, In Proc. 2nd Annual International Conference on Microtechnologies in Medicine and Biology, pp. 314–317, May 2002.
Prado M., Reina-Tosina J., Roa L., “Distributed intelligent architecture for falling detection and physical activity analysis in the elderly”, In Proc. 24th Annual IEEE EMBS Conference, pp. 1910–1911, Oct. 2002.
Fukaya K., “Fall detection sensor for fall protection airbag”, In Proc. 41st SICE Annual Conference, pp. 419–420, Aug. 2002.
Nait-Charif, H. McKenna, S.J., “Activity summarisation and fall detection in a supportive home environment”, In Proc. 17th International Conference on Pattern Recognition ICPR 2004, pp. 323–236, Aug. 2004.
Hwang, J.Y. Kang, J.M. Jang, Y.W. Kim, H.C., “Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly”, In Proc. 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2204–2207, 2004.
Shuangquan Wang, Jie Yang, Ningjiang Chen, Xin Chen, Qinfeng Zhang, “Human activity recognition with user-free accelerometers in the sensor networks”, In Proc. International Conference on Neural Networks and Brain, pp. 1212–1217, Oct. 2005.
S.-G. Miaou, Pei-Hsu Sung, Chia-Yuan Huang, “A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information”, In Proc. 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, pp.39–42, 2006.
Allen, F.R. Ambikairajah, E. Lovell, N.H. Celler, B.G., “An Adapted Gaussian Mixture Model Approach to Accelerometry-Based Movement Classification Using Time-Domain Features”, In Proc. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3600–3603, Aug. 2006.
The Sentilla Perk Pervasive Computing Kit, http://www.sentilla.com/perk.html
R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems”, Transactions of the ASME — Journal of Basic Engineering, Vol.82, Series D, pp.35–45, 1960.
Pnevmatikakis and L. Polymenakos, “Robust Estimation of Background for Fixed Cameras,” International Conference on Computing (CIC2006), Mexico City, Mexico, 2006.
Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 747–757, 2000.
Charalampos Doukas, Ilias Maglogiannis, Philippos Tragkas, Dimitris Liapis, Gregory Yovanof, “Patient Fall Detection using Support Vector Machines”, In Proc. 4th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI), Sept. 19–21, Athens, Greece.
Charalampos Doukas, Ilias Maglogiannis, “Advanced Patient or Elder Fall Detection based on Movement and Sound Data”, presented at 2nd International Conference on Pervasive Computing Technologies for Healthcare 2008.
Ian H. Witten and Eibe Frank (2005) “Data Mining: Practical machine learning tools and techniques”, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
The JAVA ME Platform, http://java.sun.com/javame/index.jspf
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Doukas, C., Maglogiannis, I., Katsarakis, N., Pneumatikakis, A. (2009). Enhanced Human Body Fall Detection Utilizing Advanced Classification of Video and Motion Perceptual Components. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds) Artificial Intelligence Applications and Innovations III. AIAI 2009. IFIP International Federation for Information Processing, vol 296. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0221-4_23
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DOI: https://doi.org/10.1007/978-1-4419-0221-4_23
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